Trends and Forecasts of Overweight Prevalence in Türkiye: A Time Series Approach Using ARIMA Models

Objective: This study aimed to fit Autoregressive Integrated Moving Average (ARIMA) models to the prevalence of overweight in Türkiye's overall, female, and male populations and to forecast future trends using the best-performing ARIMA models. Methods: The dataset comprised annual overweight prevalence values for Türkiye's overall, female, and male populations from 1974 to 2022, obtained from the World Health Organization and World Bank Group databases. The dataset was divided into training and test sets in a chronological sequence with the ratio 80:20, respectively. Training sets were used to fit ARIMA models, while test sets were used to evaluate the predictive performance of the models. Best ARIMA models were chosen based on various evaluation metrics. Results: The best models were identified as ARIMA (1,3,1) for the overall population, ARIMA (1,3,1) for females, and ARIMA (3,3,1) for males, yielding the lowest error metrics. These models effectively captured the increasing trend in overweight prevalence. Short-term forecasts indicated that the upward trend is likely to continue in the near future. Conclusion: This study contributes to a foundational understanding of the trajectory of overweight prevalence in Türkiye, providing a basis for evidence-based interventions and long-term health planning.